The development of microelectromechanical systems (MEMS) has enabled the incorporation of a wide variety of sensors into mobile devices, such as cell phones, laptops, tablets, gaming devices and other portable, electronic devices. Non-limiting examples of such sensors include an accelerometer, a gyroscope, a magnetometer, a pressure sensor, a microphone, a proximity sensor, an ambient light sensor, an infrared sensor, and the like. Further, sensor fusion processing may be performed to combine the data from a plurality of sensors to provide an improved characterization of the device's motion or orientation. However, due to the nature of electronics and mechanics, MEMS-based sensors may be prone to having bias (offset) and sensitivity errors. These errors may drift and or change due to temperature, humidity, time, assembly stress and other changes in peripheral conditions. In turn, inaccurate bias may result in decreased quality of sensor data and may complicate the sensor fusion process used to estimate parameters such as attitude (e.g., pitch, roll, and yaw), heading reference and the like which are dependent on the precision of the sensors' outputs. For example, when integration of raw data output by the sensor is used to determine velocity from acceleration or orientation angle from the rate of angular change, the bias drift problem may be significantly magnified.
In light of these characteristics of MEMS sensors, it may be desirable to perform a sensor calibration operation to characterize the bias or sensitivity error, enabling a correction of the sensor data. A sensor calibration operation may employ mathematical calculations to deduce various motion states and the position or orientation of a physical system. A sensor bias may be produced by the calibration operation, which may then be applied to the raw sensor data and calibrate the sensor. As will be appreciated, certain calibration operations may be performed effectively when the device employing the sensor is not undergoing motion. Particularly for mobile devices that may rely on a battery for energy or may have limited computational abilities, the use of sensor fusion involving multiple sensor systems may represent an undesirably large portion of the resource budget. Accordingly, the techniques of this disclosure are directed to quickly determining when appropriate calibration conditions exist so that a corresponding operation may be performed efficiently. While the following discussion is in the context of MEMS sensors as used in portable devices, one of skill in the art will recognize that these techniques may be employed to any suitable sensor application as desired.